Update README.md

README.md

@@ -10,12 +10,167 @@ language:
 - en
 ---
 
+# DeepSeek OCR - Fine-tuned for German/Deutsch
+
+This model is a fine-tuned version of DeepSeek OCR on German text for Optical Character Recognition (OCR) tasks.
+
+## Model Description
+
+- **Base Model:** DeepSeek OCR
+- **Language:** German (de)
+- **Task:** Image-to-Text (OCR)
+- **Training Data:** 200K synthetic German text images
+- **License:** Apache 2.0
+
+This model has been fine-tuned specifically for recognizing German text in images, including handling of German-specific characters (ä, ö, ü, ß) and common German compound words.
+
+## Intended Uses
+
+This model is designed for:
+- Extracting German text from scanned documents
+- Digitizing printed German materials
+- Reading German text from photographs
+- Processing German forms and receipts
+- Any German text recognition tasks
+
+## How to Use
+
+### Basic Usage
+
+```python
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+import requests
+
+# Load model and processor
+processor = TrOCRProcessor.from_pretrained("YOUR_USERNAME/deepseek-ocr-german")
+model = VisionEncoderDecoderModel.from_pretrained("YOUR_USERNAME/deepseek-ocr-german")
+
+# Load image
+url = "path_to_your_german_text_image.jpg"
+image = Image.open(url).convert("RGB")
+
+# Process
+pixel_values = processor(image, return_tensors="pt").pixel_values
+generated_ids = model.generate(pixel_values)
+generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+print(generated_text)
+```
+
+### Batch Processing
+
+```python
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+
+processor = TrOCRProcessor.from_pretrained("YOUR_USERNAME/deepseek-ocr-german")
+model = VisionEncoderDecoderModel.from_pretrained("YOUR_USERNAME/deepseek-ocr-german")
+
+# Multiple images
+images = [Image.open(f"image_{i}.jpg").convert("RGB") for i in range(5)]
+
+# Batch process
+pixel_values = processor(images, return_tensors="pt", padding=True).pixel_values
+generated_ids = model.generate(pixel_values)
+generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+for text in generated_texts:
+    print(text)
+```
+
+### With GPU Acceleration
+
+```python
+import torch
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+processor = TrOCRProcessor.from_pretrained("YOUR_USERNAME/deepseek-ocr-german")
+model = VisionEncoderDecoderModel.from_pretrained("YOUR_USERNAME/deepseek-ocr-german").to(device)
+
+image = Image.open("german_text.jpg").convert("RGB")
+pixel_values = processor(image, return_tensors="pt").pixel_values.to(device)
+
+generated_ids = model.generate(pixel_values)
+text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(text)
+```
+
+## Training Details
+
+### Training Data
+
+The model was fine-tuned on a synthetic German OCR dataset containing 200,000 images with:
+- Diverse German sentences covering multiple domains (everyday conversation, news, literature, technical, business)
+- Various fonts and font sizes (16-48pt)
+- Multiple augmentations: noise, blur, brightness/contrast variations
+- Different text and background colors
+
+**Data Split:**
+- Train: 180,000 samples (90%)
+- Validation: 10,000 samples (5%)
+- Test: 10,000 samples (5%)
+
+### Training Framework
+
+```python
+# Example training configuration
+from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments
+
+training_args = Seq2SeqTrainingArguments(
+    output_dir="./deepseek-ocr-german",
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    learning_rate=5e-5,
+    num_train_epochs=10,
+    logging_steps=100,
+    save_steps=1000,
+    eval_steps=1000,
+    evaluation_strategy="steps",
+    save_total_limit=2,
+    fp16=True,
+    predict_with_generate=True,
+)
+```
+
+# # Limitations
+
+- **Font coverage:** Performance may vary with handwritten text 
+- **Image quality:** Works best with clear, high-contrast images
+- **Domain specificity:** Best performance on printed German text similar to training distribution
+
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{deepseek-ocr-german,
+  author = {Santosh Pandit},
+  title = {DeepSeek OCR - German Fine-tuned},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/YOUR_USERNAME/deepseek-ocr-german}},
+}
+```
+
+## Model Card Contact
+
+For questions or feedback, please open an issue on the model repository or contact [hello@neuralabs.one].
+
+---
+
+### Acknowledgments
+
+- Base model: DeepSeek AI
+- Training data generation: LM Studio with local LLM
+- Framework: Hugging Face Transformers
+
 # Uploaded finetuned  model
 
 - **Developed by:** neuralabs
 - **License:** apache-2.0
-- **Finetuned from model :** deepseek-ai/DeepSeek-OCR
-
-This deepseek_vl_v2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+- **Finetuned from model :** deepseek-ai/DeepSeek-OCR